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To monitor an LLM application in production, trace each user request across your application, retrieval system, model calls, tools, and orchestration; record consistent timing, status, model, and token fields; and add separate checks for answer quality and data handling. A model-call log alone cannot show whether a slow or failed response came from retrieval, a retry, a tool, or the model.
What LLM observability needs to show
For a useful diagnosis, you need to connect a user-visible request to the work that produced its answer. Represent the request as a root trace and each meaningful operation as a related span. A typical trace may include ingress, application logic, retrieval or vector search, one or more model calls, tool execution, retries, and post-processing.
That structure lets an engineer follow the request in sequence, compare step durations, and locate errors. It also helps distinguish a slow model response from a slow retrieval call or a tool that failed before the next model call. AWS documents hierarchical traces for these kinds of operations; the precise view and supported fields depend on the instrumentation and backend.
Tracing answers where and when work happened. It does not, by itself, establish whether an answer was correct, relevant, or safe. Operational monitoring and answer-quality evaluation belong in the same production feedback loop, but they are different signals.
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Plan instrumentation around the request path
Map the work before choosing a dashboard
Start with a representative request and draw its path from entry to response. Mark application and orchestration boundaries, retrieval, provider calls, tool execution, retries, and asynchronous work. Use that map to decide where root and child spans begin and end. If work continues asynchronously, propagate trace context where the framework and transport support it so that later operations remain connected to the originating request.
Record enough structured metadata to understand what happened, but do not treat full prompts, retrieved documents, tool arguments, or outputs as mandatory trace fields. The right level of content depends on the troubleshooting need and the data the application handles.
Adopt a stable schema and validate its mapping
OpenTelemetry is a practical foundation when it fits the existing stack. Add GenAI-specific attributes for the operation, provider or system, requested model, token usage, and tool or agent operations. Keep ordinary span fields too: trace and parent IDs, timestamps, duration, and status. Useful application-specific attributes can include deployment version, environment, workflow or feature, and a privacy-safe request correlation ID.
Keep high-cardinality or identifying values out of metric dimensions where possible. A user identifier or unique request ID may be useful on an access-controlled trace, but multiplying metric series by such values can make metrics costly and difficult to operate.
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GenAI conventions, SDKs, and backend mappings evolve. Pin the versions used in deployment, document the intended fields, and verify that the collector and receiving backend preserve them. For example, Datadog’s article published on 2025-12-01 describes support for OpenTelemetry GenAI conventions v1.37 and later; that is a dated vendor compatibility statement, not a permanent minimum for every product or destination. AWS’s OpenSearch example demonstrates registering an OpenTelemetry trace provider and exporter and attaching model and token attributes.
Monitor operational behavior and investigate with traces
Start with signals tied to user impact
Track request volume, errors, end-to-end latency, and latency by important step. Add provider and model dimensions where they help explain changes, plus input and output token counts. Estimate cost only when the relevant pricing data is reliable and the calculation accounts for the usage being measured; label it as an estimate rather than an invoice total.
Break down metrics by dimensions such as model, provider, application version, route, or environment only when the resulting series remain manageable and do not expose sensitive identifiers. Dashboards are most useful when they help answer operational questions: which route is slower, whether errors began after a version change, and whether a particular provider or tool is failing.
Alert on actionable conditions
Alert on sustained user-facing symptoms and conditions someone can act on: a meaningful latency increase, error-budget deterioration, provider failures, unusual token or estimated-cost spikes, or missing telemetry. Avoid paging on every isolated low-quality answer. Use a metric anomaly to find relevant traces, then inspect individual executions to see where they diverged.
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LangSmith describes dashboards for token usage, P50 and P99 latency, errors, cost breakdowns, and feedback. These are examples of vendor features, not a universal dashboard specification; choose views around your own service objectives and investigation workflow.
Measure answer quality separately from system health
Maintain repeatable evaluations
Keep a versioned set of representative tasks and known failure cases. Run repeatable offline evaluations when changing prompts, models, retrieval configuration, or tools. For production, evaluate a selected sample or high-risk flows rather than assuming every response can be judged automatically.
Use deterministic checks when expected behavior is crisp: whether an output matches a required schema, includes required fields, or respects tool permissions. For semantic qualities such as relevance or correctness, use carefully designed model-based evaluations, human review, or both. An evaluator can also be wrong, so monitor agreement and false positives instead of treating its score as ground truth.
Use traces to improve the evaluation set
Production traces can reveal new edge cases and failure patterns. After review, convert appropriate examples into curated evaluation data so future prompt, model, retrieval, and tool changes can be checked against them. Datadog documents a workflow for promoting traces into version-controlled datasets and comparing prompts, parameters, models, and agent strategies; LangSmith describes online evaluation as a monitoring option. These are product capabilities, not guarantees about evaluation quality.
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For uncertain or consequential cases, route a suitable sample to human review. Define which cases qualify, who can access their contents, and how reviewer feedback becomes a measurable signal.
Protect prompts, outputs, and telemetry copies
Prompts and responses may contain sensitive information, but so can retrieved context, tool arguments, and tool results. Decide what the team genuinely needs to retain before exporting traces. Minimize content, omit secrets, redact or anonymize fields that must be retained, restrict access, and set retention and deletion rules.
OWASP’s LLMX Cornucopia guidance recommends: “Log only the minimum AI interaction metadata needed for security monitoring, and ensure any prompt or output content included in logs is minimized and redacted or anonymized before storage.” It also recommends detecting AI-specific attack patterns and monitoring and alerting on abuse.
When feasible, filter or redact telemetry in an OpenTelemetry Collector or another controlled gateway before it leaves the application network. Review the entire data path, not only the primary observability store: errors, dead-letter queues, backups, support access, and third-party processors can create additional copies. Limit access to traces that retain content and verify that retention and deletion policies cover those copies.
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Provider-side data handling is separate from the traces your application stores. OpenAI’s current API data-controls documentation, accessed 2026-10-07, says default abuse-monitoring logs may include prompts and responses and are retained for up to 30 days, subject to legal exceptions and endpoint- or account-specific details. Eligible organizations may apply for modified abuse monitoring or zero data retention, with limitations. This provider policy does not determine the retention of independently stored observability data.
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There is no single required vendor. Compare options using representative traces and the constraints of your environment, not feature lists alone. Vendor documentation can substantiate a vendor’s own feature claims, but the options below are not an independent head-to-head performance ranking.
| Implementation path | When it may fit | What to verify |
|---|---|---|
| OpenTelemetry with an existing observability stack | Useful when common instrumentation and correlation with existing service traces matter. | Confirm how GenAI fields are mapped and whether the backend can navigate nested model, retrieval, and tool spans. |
| Dedicated LLM or agent observability platform | Worth evaluating when nested trace views, annotation, evaluation, or prompt workflows are central to the team’s work. | Check framework and provider coverage, evaluation workflow, privacy and deployment options, export, and operating cost. LangSmith documents these feature categories and OpenTelemetry integration. |
| Cloud-native observability service | May fit a team whose infrastructure, access policies, and operational practices already align with that cloud environment. | Check the data path, authentication, integrations, query model, infrastructure ownership, and whether agent and retrieval traces are sufficiently detailed. AWS documents an OpenTelemetry Collector-to-OpenSearch architecture and GenAI agent trace views. |
Across any path, assess framework and provider coverage, trace fidelity, metric-to-trace navigation, evaluation and human-review workflow, access controls, deployment and regional options, retention, interoperability, query usability at expected volume, and full operating cost. Keep the ability to export or process telemetry in mind if policy requirements or platform choices change.
Validate the rollout and failure behavior
- Test in development or staging. Send a known request through retrieval, model, and tool paths. Inspect that the trace has the expected parent-child structure, trace context, provider and model fields, token counts, durations, and error status.
- Check privacy controls with realistic data shapes. Confirm redaction and omission rules cover prompts, retrieved content, tool inputs and results, outputs, and error paths before the data is stored or exported.
- Exercise retries, sampling, and exporter failures. Make sure retries appear as distinct work in the trace, sampling does not eliminate the rare high-risk events you need to investigate, and a telemetry destination outage does not break user requests.
- Confirm access and retention behavior. Check who can view traces, how long each copy is retained, and how deletion and backup policies apply.
- Roll out progressively. Watch telemetry volume and operating cost as coverage expands, and document a fallback for periods when the observability destination is unavailable.
A rollout is complete when operators can follow a real request to the failing or slow step, quality checks can surface meaningful regressions, and telemetry handling matches the application’s privacy requirements.
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